A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

Truthful auction mechanisms for resource allocation in the Internet of Vehicles with public blockchain networks

Cloud computing is characterized by strong computing and storage capabilities, and edge computing has the advantages of low latency and low power consumption. Many service providers have begun to combine the advantages of cloud and edge computing to provide better quality of service, but the heterogeneity of cloud and edge computing represents a challenge for service deployment and resource allocation. This paper proposes a framework for cloud-edge collaboration based on live video webcast services and transforms the resource allocation problem into a constrained integer programming (IP) model. Additionally, we introduce an auction mechanism to solve the problem of resource competition among the anchor users in live services. By solving the IP resource allocation problem and Vickrey–Clarke–Groves mechanism, we obtain the optimal resource allocation mechanism. Based on the dominant resource proportion and matching model, we design a resource allocation mechanism for the online environment. These mechanisms can be used for reservation and live webcast scenarios. Furthermore, we prove that the two mechanisms have individual rationality and truthfulness. Our approach is characterized by high social welfare, high resource utilization and a short execution time.

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

Resource management (RM) is a challenging task in a cloud computing environment where a large number of virtualized, heterogeneous, and distributed resources are hosted in the datacentres. The uncertainty, heterogeneity, and the dynamic nature of such resources affect the efficiency of provisioning, allocation, scheduling, and monitoring tasks of RM. The most existing RM techniques and strategies have insufficiency in handling such cloud resources dynamic behaviour. To resolve these limitations, there is a need for the design and development of intelligent and efficient autonomic RM techniques to ensure the Quality-of-Service (QoS) of cloud-based applications, satisfy the cloud user requirements, and avoid a Service-Level Agreement (SLA) violations. This paper presents a comprehensive review along with a taxonomy of the most recent existing autonomic and elastic RM techniques in a cloud environment. The taxonomy classifies the existing autonomic and elastic RM techniques into different categories based on their design, objective, function, and applications. Moreover, a comparison and qualitative analysis is provided to illustrate their strengths and weaknesses. Finally, the open issues and challenges are highlighted to help researchers in finding significant future research options.

Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis

Cloud computing offers a vast number of processing opportunities and heterogeneous resources and meets the requirements of numerous applications at various levels. Thus, the allocation and management of resources are vital in cloud computing. Resource allocation is a technique in which the available resources such as central processing unit, random-access memory, storage, and network bandwidth in cloud data centres are divided among users in a way that facilitates resource utilisation, provider profit, and user satisfaction. Integration and interaction with other modules of the resource management system, security, privacy, fairness, non-fragmentation of resources, resource utilisation, provider profit, user satisfaction, reducing energy consumption, load balancing, flexibility, scalability, availability, improvement the number and time of virtual machine migrations, and the number of overloaded resources are considered as challenges for the resource allocation mechanism. A systematic resource allocation survey with innovations in resource management system architecture, categorising mechanisms, addressing the challenges, and issues is presented. In addition to introducing the existing resource allocation mechanisms, other similar survey papers have been reviewed. Finally, there are some suggested topics for future work.

Resource allocation mechanisms in cloud computing: a systematic literature review

An online auction mechanism for time-varying multidimensional resource allocation in clouds

Time-varying resource allocation allows users to define their own unique resource requirement plans during different time periods. This mode of allocation can increase the flexibility of resource usage and reduce resource usage costs for users. Moreover, combining this approach with an auction mechanism can enable resource providers to obtain greater social welfare and benefits; therefore, such resource allocation has become a hot topic in cloud computing. This paper addresses the problem of time-varying batch virtual machine (VM) allocation and pricing in the cloud. Specifically, (1) we propose a novel integer programming model for the time-varying batch VM allocation problem, and (2) we design two truthful auction mechanisms to solve the allocation and pricing problem in a competitive environment. The optimal mechanism includes a dynamic programming (DP)-based resource allocation algorithm and a Vickrey–Clarke–Groves (VCG)-based payment price algorithm. Meanwhile, we also design a greedy mechanism that includes a dominant-resource-based allocation algorithm and a dichotomy-based payment price algorithm. We prove the economic characteristics, including truthfulness and individual rationality, of the above two mechanisms. Furthermore, we prove the approximation ratio of the allocation algorithm in the greedy mechanism. Compared to state-of-the-art research, our approach is characterized by high social welfare, a high served user ratio and a short execution time.

Strategy-proof mechanism for time-varying batch virtual machine allocation in clouds

Task offloading is a major problem in edge computing. In existing research, tasks are generally portrayed as requiring specific resources and time, and the task owner provides the value that he is willing to pay. The goal of the resource provider is to obtain the maximum social welfare and profit. However, this approach cannot cover all task offloading scenarios. 1) This paper innovatively considers a continuous task offloading problem in mobile edge computing in which the task can be partially executed and the task owner provides a nonlinear value function to pay for the executed task. The resource provider needs to decide which tasks to execute at each moment in order to obtain the maximum social welfare and profit. We represent the problem as a nonlinear integer programming model with multiple resource constraints. 2). We design an auction mechanism to solve the continuous task offloading problem in a competitive environment. Specifically, we propose a resource allocation algorithm based on the remaining value strategy and a payment price algorithm based on the critical value theory to guarantee truthful task information. Our approach is experimentally compared with existing research in terms of execution time, social welfare, and resource utilization.

A Truthful Auction Mechanism for Cumulative Resource Allocation in Mobile Edge Computing

While the issues of high-speed rail (HSR) tariff and its impact on equity have always been a hot topic in China, which also attracted the attention of international researchers, research on the level of HSR tariff from the perspective of individual affordability and equity issue have scarcely been considered. This study therefore aims to fill the research gap by quantifying the HSR relative tariff level and its impact on income/regional distribution via introducing a HSR affordability index. Based on the index, we calculate the HSR affordability levels of the main international sample countries with the order of magnitude. Then we expand the analysis of HSR affordability level according to differentiated HSR pricing practice in the selected countries. At the same time, we compare the HSR tariff impact on economic equity among different countries, regions, and social classes. The result indicates that although the absolute level of HSR tariff is low, China is generally in the middle/the lowest and unbalanced position among the sample countries on HSR affordability in terms of expensiveness and economic equity. When we considered the differentiated tariff policy implemented in Europe and Japan, the affordability gap between China and the other countries is further enlarged. Moreover, inequitable distribution of HSR affordability between different income groups and social classes are found larger not only within China, but also higher when compared to the other sample countries. Finally, we conclude that China should implement differentiated HSR pricing system at a larger scale and greater extent based on the Ramsey pricing and Yield management principle to grasp the additional revenue from the high level income group/the developed region and alleviate the economic equity as well. Meanwhile, we also suggest the balance between HSR affordability for passengers themselves and the macro financial affordability for the country as a whole should also be considered in the future HSR pricing regime formulation. • The HSR affordability level and its impact on economic equity are quantified by establishing a HSR affordability index. • Although the HSR tariff level is low, China is in the middle/lowest level among the sample countries on HSR affordability. • In terms of economic equity, China is generally in the unbalanced position among the sample countries. • China should implement differentiated HSR pricing system at a larger scale and greater extent. • China should implement differentiated HSR pricing system at a larger scale and greater extent. 

Xutao Yang

Papers

An online auction mechanism for time-varying multidimensional resource allocation in clouds

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

Strategy-proof mechanism for time-varying batch virtual machine allocation in clouds

A Truthful Auction Mechanism for Cumulative Resource Allocation in Mobile Edge Computing

Are China's HSR tariff affordable and economic equitable? An international comparison perspective